Sectors Performance
Sector Price Performance Distribution
For Date: 2026-08-21
Performance Heatmap (%)
| Sector | 1 Day | 1 Week | 1 Month | 3 Months | 6 Months | YTD | 1 Year |
| Materials | 2.14 | 2.49 | 6.87 | 7.42 | 1.92 | 17.03 | 20.61 |
| Health Care | 1.29 | 4.53 | 8.97 | 18.39 | 11.08 | 13.25 | 29.65 |
| Consumer Discretionary | 1.15 | 1.09 | 2.74 | -0.38 | 3.05 | 0.12 | 5.14 |
| Financials | 0.93 | -0.17 | 2.44 | 11.50 | 14.27 | 5.55 | 10.77 |
| Consumer Staples | 0.79 | 1.55 | 2.30 | 2.27 | -2.13 | 12.07 | 7.15 |
| Communication Services | 0.65 | 0.52 | 1.25 | -3.80 | -2.88 | -4.16 | 2.80 |
| Industrials | 0.27 | -3.26 | 0.89 | 5.95 | 3.64 | 14.69 | 21.16 |
| Technology | 0.11 | -3.68 | 1.40 | 2.76 | 32.66 | 27.34 | 42.34 |
| Real Estate | 0.00 | 0.56 | -0.27 | 2.20 | 4.98 | 13.38 | 11.89 |
| Energy | -0.17 | 1.69 | 8.79 | 8.40 | 16.96 | 41.33 | 51.92 |
| Utilities | -2.28 | -3.19 | -4.79 | -4.36 | -7.14 | 0.38 | 2.74 |
Ask the market a question. Get a calculated answer.
The AI is not a chatbot bolted onto a document store. It calls the same analytics engine that powers every screen on this platform — so what comes back is a number it computed from raw history, with the command that produced it.
86,000+ instruments
Global equities, ETFs, funds, options, FX, commodities, crypto, economics, filings, transcripts and news — one normalised symbol universe with adjusted history.
A real analytics engine
Screening, backtesting, technicals, options analytics, correlations, seasonality and factor models — computed on demand from raw prices, never a stale cache.
It shows its working
Answers arrive with the charts, tables and tool calls behind them, so you can check the number instead of trusting a paraphrase.
Your own documents
Upload filings, decks and research. Ask across them and the answer cites the page it came from.
Agents and workflows
Multi-step research that runs the platform's tools for you — screen, pull the history, compute, compare, then write it up.
MCP, CLI and API
The same command catalogue from Claude, your own agent, a shell or your pipeline. The answer on screen is the answer your job gets at 4am.
You ask
“How does NVDA usually trade through earnings?”
It calls
→ ka.options_expected_move(NVDA)
It answers
NVDA has averaged a 9.2% absolute move on the day after earnings and closed higher 67% of the time. Two in three reactions land between −4.2% and +16.3% — the distribution is skewed right, not symmetric.
Every figure computed live from our own history — not scraped, not summarised.
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